{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 前言：\n",
    "在第1章中，我们提到最常见的监督学习任务是**回归**（预测值）和**分类（**预测类）。 在第2章中我们探索了回归任务，使用各种算法，如线性回归，决策树和随机森林（将在后面的章节中进一步详细说明）。 现在我们将注意力转向**分类**系统。\n",
    "\n",
    "#### MNIST\n",
    "在本章中，我们将使用MNIST数据集，这是由高中学生和美国人口普查局的雇员手写的一组70,000个小数字图像。每个图像都标有它所代表的数字。 这个集已经被研究了很多次，它通常被称为机器学习的“Hello World”：每当人们想出一个新的分类算法时，他们都很想知道它将如何在MNIST上表现。 每当有人学习机器学习时，他们迟早会解决MNIST问题。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0.Setup\n",
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "# 让这份笔记同步支持 python 2 和 python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "# 让笔记全程输入稳定\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "# 导入绘图工具\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "# 设定图片保存路径，这里写了一个函数，后面直接调用即可\n",
    "PROJECT_ROOT_DIR = \"F:\\ML\\Machine learning\\Hands-on machine learning with scikit-learn and tensorflow\"\n",
    "CHAPTER_ID = \"Classification_MNIST_03\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "# 忽略无用警告\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. MNIST\n",
    "Scikit-Learn提供了许多辅助函数来下载流行的数据集。 MNIST是其中之一。 以下代码获取MNIST数据集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'COL_NAMES': ['label', 'data'],\n",
       " 'DESCR': 'mldata.org dataset: mnist-original',\n",
       " 'data': array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       " 'target': array([0., 0., 0., ..., 9., 9., 9.])}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用Scikit-Learn下载的数据集通常有一个类似于字典的结构：\n",
    "* 一个'**DESCR**'关键字用来描述数据集\n",
    "* 一个'**data**'关键字包含一个数组，这个数组的每一行代表一个例子，每一列代表一个特征\n",
    "* 一个'**target**'关键字，包含一个数组，数组内容是例子的标签 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X是数据集，是一个形状为 70000 x 784 的数组：70000行，784 列。 每一行代表了一个例子，有70000张图片，即70000个例子，每个例子有784个特征，这是因为每张图片有 28 x 28 个像素，每个特征代表了每个像素的强度，像素强度范围是[0,255] 下面的代码是查看其中一张图片，我们需要将它重新转化为28 x 28 的图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221360b67f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "some_digit = X[36000]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap = matplotlib.cm.binary,\n",
    "interpolation=\"nearest\")\n",
    "\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[36000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = matplotlib.cm.binary,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXTRA\n",
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure more_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213958b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "save_fig(\"more_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should always create a test set and set it aside before inspecting the data closely. The MNIST dataset is actually already split into a training set (the first 60,000images) and a test set (the last 10,000 images):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle the training set; this will guarantee that all cross-validation folds will be similar \n",
    "import numpy as np\n",
    "\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Training a Binary Classifier\n",
    "\n",
    "下面建立一个二分类器，识别是5 或者不是5，我们选择的分类器是**SGD**，即Stochastic Gradient Descent。SGD的优势是能够有效的处理大型数据，原因之一是 SGD在每一时刻只处理一个训练例子，这也导致它适合于 **online learning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)# True for all 5s, False for all other digits.\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=42, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=5, random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用刚才得到的5进行验证\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Performance Measures\n",
    "Evaluating a classifier is often significantly trickier than evaluating a regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Measuring Accuracy Using Cross-Validation\n",
    "\n",
    "使用交叉验证集评估模型是一个不错的选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9502 , 0.96565, 0.96495])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Implementing Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9502\n",
      "0.96565\n",
      "0.96495\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, random_state=42)\n",
    "\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds = X_train[train_index]\n",
    "    y_train_folds = (y_train_5[train_index])\n",
    "    X_test_fold = X_train[test_index]\n",
    "    y_test_fold = (y_train_5[test_index])\n",
    "\n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct / len(y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "超过95%的准确率，这看起来的确不错，但是现在高兴为时过早，让我们看一个非常愚蠢的分类器，它只是对“not-5”类中的每个图像进行分类："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X), 1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.909  , 0.90715, 0.9128 ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "never_5_clf = Never5Classifier()\n",
    "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "超过90%的准确率，好像也很高。之所以出现这样的情况是因为在MNIST数据集中，数字5 占了10%左右，如果我们一直猜测不是数字5的概率，那么我们的准确率几乎会一直高于90%。\n",
    "\n",
    "这个现象证明了对于**classifiers**为什么**准确率**通常不是首选的性能指标，尤其是在处理偏斜数据集时（即，当某些类比其他类更频繁时）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Confusion Matrix-混淆矩阵\n",
    "\n",
    "评估**classifiers**性能一个比较好的方法是查看混淆矩阵。 一般的想法是计算A类实例被分类为B类的次数。例如，要知道分类器将5与3混淆的次数，您将查看混淆矩阵的第5行和第3列 \n",
    "\n",
    "要计算混淆矩阵，首先需要有一组预测，以便他们可以与实际目标进行比较。 您可以对测试集进行预测，但现在让它保持不变（请记住，只有在项目的最后才能使用测试集，一旦有了准备启动的分类器）。 相反，您可以使用**cross_val_predict（）**函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就像**cross_val_score（）**函数一样，**cross_val_predict（）**执行K-fold交叉验证，但不是返回评估分数，它返回每个test fold 上的预测。 这意味着您可以获得训练集中每个实例的clean 预测（“clean ”意味着预测是由在训练期间从未看过数据的模型进行的）。\n",
    "\n",
    "现在我们可以通过**confusion_matrix()** 来获得混淆矩阵了，只要把目标类 ( y_train_5 ) and 预测类( y_train_pred )作为参数传递给它就可以:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53272,  1307],\n",
       "       [ 1077,  4344]], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "                      \n",
    "negative class  [[ **true negatives** , **false positives** ],\n",
    " \n",
    "positive class  [ **false negatives** , **true positives** ]]\n",
    "\n",
    "第一行考虑**非5**的图片，**negative class** ：\n",
    "\n",
    "* true negatives:  53,272 张被正确的分类为非5\n",
    "* false positives：1307张被错误的分类为5\n",
    "\n",
    "第一行考虑**是5**的图片，**positive class** ：\n",
    "\n",
    "* false negatives：1077张错误的分类为非5\n",
    "* true positives： 4344张被正确的分类为5\n",
    "\n",
    "一个完美的分类器应该只有**true positives** 和 **true negatives**, 即主对角线元素不为0，其余元素为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_perfect_predictions = y_train_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision and Recall\n",
    "\n",
    "\n",
    "**$ precision = \\frac {TP} {TP + FP} $**\n",
    "\n",
    "\n",
    "**$ recall = \\frac {TP} {TP + FN} $**\n",
    "\n",
    "\n",
    "* TP is the number of true positives\n",
    "* FP is the number of false positives\n",
    "* FN is of course the number of false negatives.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7687135020350381"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.801328168234643"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将**Precision** 和 **Recall**结合到一个称为**F1 score** 的量,，特别是在我们需要一种简单的方法来比较两个分类器。**F1 score**是精度和召回的调和平均值。常规均值平等地处理所有值，但调和平均值给予低值更多权重。 因此，如果召回和精确度都很高，分类器将只获得高F 1分数。\n",
    "\n",
    "$ F_1  = $ $2\\over {1\\over precision}+{1\\over recall} $ $=$ $2×$ $precision×recall\\over precision+recall $ $=$ $TP\\over {TP}+{FN + FP\\over 2}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7846820809248555"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$F_1$评分倾向于具有相似精度和召回率的分类器。这并不总是你想要的：在某些情况下，你最关心的是精确度，而在其他情况下，你真的很关心召回。例如：\n",
    "* 如果您训练分类器来检测对孩子来说安全的视频，您可能更喜欢拒绝许多好视频的分类器（低召回率）但只保留安全分类（高精度），而不是一个具有更高召回率的分类器，这会让一些非常糟糕的视频出现在您的产品中（在这种情况下，您甚至可能希望添加人工管道来检查分类器的视频选择）。\n",
    "\n",
    "* 另一方面，假设你训练一个分类器来检测监控图像上的商店扒手：如果你的分类器只有30％的精度，只要它有99％的召回率就可能很好（当然，保安人员会得到一些虚假警报，但几乎所有商店扒手都会被抓住）。\n",
    "\n",
    "不幸的是，你无法双管齐下：提高精确度，减少召回率，反之亦然。这称为精确/召回权衡。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Precision/Recall Tradeoff\n",
    "\n",
    "为了理解这种权衡，让我们看一下**SGDClassifier**是如何做出它的分类决定的。对于每个实例，它根据决策函数计算得分，如果该得分大于阈值，则将实例分配给**positive class**，否则将其分配给**negative class**。\n",
    "\n",
    "下图显示了一些数字，从左侧最低分到右侧最高分。假设决策阈值位于中心箭头（两个5s之间）：您将在该阈值的右侧找到4个**true positives**（实际5s），和一个**false positive**（实际为6）。因此，使用该阈值，**精度**为80％（5个中的4个）。但是在6个实际数字5中，分类器仅检测到4个，因此**召回率**为67％（6个中有4个）。\n",
    "\n",
    "现在，如果你提高阈值（将其移动到右边的箭头），**false positive**（6）变为**true negative**，从而提高**精度**（在这种情况下高达100％），但一个**true positive** 变为**false negative**，将**召回率**降低至50％。相反，降低阈值会**增加召回率**并**降低精度**。"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Figure 3-3. Decision threshold and precision/recall tradeoff](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn不允许您直接设置阈值，但它确实可以让您访问用于进行预测的决策分数。 您可以调用其**decision_function（）**方法，而不是调用分类器的**predict（）**方法，该方法返回每个实例的分数，然后使用您想要的**任何阈值**根据这些分数进行预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([161855.74572176])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 200000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "那么如何决定使用哪个阈值？ 为此，您首先需要再次使用**cross_val_predict（)** 函数获得训练集中所有实例的分数，但这一次你希望它返回决策分数而不是预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
    "                             method=\"decision_function\")\n",
    "\n",
    "y_scores.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在有了这些分数，您可以使用**precision_recall_curve（）** 函数计算所有可能的精度和召回率的阈值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，您可以使用Matplotlib绘制**精度**和**召回率**作为阈值的函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure precision_recall_vs_threshold_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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dfGlZqiUAn1T9hD2X97D13FZm/jOT4RuHM3yj/v6omrcq7cu2541ib+DnZoWb32QSM2fqcNOxo16dNUsWsysSwvZZxTo4//77L8WLF0/HitLe7RacY8eOERgYmHz81VdfZffu3UREPLjwnGEYJCQk4OTklJ6lZhi2+H3wLE6EnmDT2U0cunqISXsncS36Gt4u3nR5sQt9KvWRoGOy06f1FO6pU+G99/RePBaLHnNz/0aOQoinl9J1cGS75gymQoUK3Lhxg5CQEPLnz0/btm2ZOHEixYoVw8nJiWW3Nk2Jjo6mf//+FChQACcnJwoUKMAXX3yBxWK553pXr16le/fu5MmTB2dnZ/LkyUO7du2Ii9MbvA8dOvSBMR2jR4+mePHiuLq64u3tTVBQEAsWLEh+vnr16sndVLcdOXKEJk2a4OXlhaurK8HBwaxYseKec26/17Fjx2jQoAEeHh7ky5eP4cOHP1C3eLRCPoXoWK4j39T6hkt9LrHp7U1UzluZEVtGkG1kNoqOKUrP5T3ZcWGH2aVmGoahF90rW1Yv///22zrkrFunn7ezk3AjRHqz+i6q+37PAnrQXt++6fN8ajt16hT29vZ4eOiZM+vWrWPv3r0MGTIEf39/8ufPT2JiInXq1OHQoUN8+umnlC5dmm3btvHZZ58RGhrKd999B0BYWBiVKlUiNDSUQYMGUaZMGUJCQli0aBHx8fE4P2RL1xkzZtCnTx8GDx5M1apViYmJYf/+/YSGhj6y5osXL1KlShU8PT0ZM2YMWbNmZezYsTRo0IClS5dS775pIE2aNOHtt9+md+/eLFmyhCFDhpAnT557xiSJlHGwc6BK3ipUyVuFPZf2MO/feWw9v5XxO8czbsc4aheqzSv5XqFFyRYE+gQ++YLiqSQl6e0OXF31VO79+/XCevXqwYsv6s0ohRDmsPqAY+2SkpJITEzk5s2bzJ49m/nz5/P666/j5uYG6JCya9eue1ZynjZtGps3b2bDhg1Uu7Xr3WuvvQbAsGHD6N+/P/7+/owaNYqTJ0+yc+dOypcvn/z61q1bP7KerVu3UqZMGQYPHpx8rH79+o/9DN9//z1hYWFs3bo1ubutfv36lChRgk8++eSBgNOnT5/kMFOzZk3Wrl3Lb7/9JgHnOZXPUZ7yOfS/87XoawxeN5g/T/3JyhMrGbh2IJXyVOLNEm/SNairDE5+TocO6R2oP/oIPv8cPvkEOnWCd94xuzIhxG1WH3CeNJEnrZ9/XsWKFUu+b2dnR5s2bfjhhx+SjwUHBz+wTcWKFSvIly8flSpVumd139q1azNo0CC2bdtGo0aNWLVqFRUqVLgn3DxJhQoVGDduHL169aJx48ZUqlQpOWw9ysaNGwkODr5nLJG9vT2tW7dm+PDh3Lhxgyx3japs0KDBPa8vVaoUe/bsSXGN4sn83PwY10APBj8dfpqJeyYyff90/rPyPwxZP4SifkXp/EJnWpdujZvj4/99xb1GjtTB5rYcOfRXmb0vRMYiY3BMtmDBAnbs2MHhw4eJiopi6tSp+Pj4JD+f4/ZPz7uEhIRw5swZHB0d77m99NJLAMl7Nl2/fp3cT9lG3r59e3766Se2b99OnTp18PHxoWnTppw+ffqRrwkNDX1onQEBARiGQVhY2D3H7/58AM7OzsTGxj5VnSLl8nvlZ/irwzn5wUmWtF5CixItiE2M5d0l7+L3jR/tF7Rn7am1JFmSzC41wzOMO+Fm+3ZISNAtN0KIjMfqW3CsXalSpe5p+bjfwxZ18/X1pUCBAsyePfuhr8mfPz8Afn5+XLhw4anqUUrRpUsXunTpQlhYGKtWraJPnz60bNmS7du3P/Q1Pj4+XL58+YHjly9fRiklu8BnIA2LNKRhkYYYhsGyY8uYeWAm8/+dz7T90yjiW4QmxZpQN7AuFXNXlG6sW+LidJjJl0/fPvgA3n0XSpUyuzIhxONIC44Vqlu3LufOncPDw4OgoKAHbn5+eppw7dq1+fvvv9m3b98zvY+3tzctW7bkzTff5J9//nnkea+88grbtm27p5UnKSmJWbNmUb58+Xu6p0TGoJSiYZGGzGw2k4t9LvJTg59wdXDl6y1f8+qUV/H/1p9P/vyE+KR4s0s11dSpkDMnvPIK/PKLPvbDDxJuhLAG0oJjhdq0acOkSZN47bXX6NOnD2XLliU+Pp4TJ06wePFiFi5ciJubG71792bmzJnUrFmTQYMGUbp0aa5du8aiRYsYP348np6eD1y7c+fOeHp6UrFiRfz9/Tl69CjTpk2jdu3aD6lE6927N5MnT6ZWrVoMGzaMLFmyMG7cOI4ePZo8rV1kXFmcs9A1qCtdg7oSEhXCX+f+YsaBGXy5+Uum7Z9Gv0r96FCuA1mcM09QvXYNihfXX5WCGTOgeXOzqxJCPA0JOFbI0dGRlStXMmLECCZMmMCpU6dwd3enUKFCNGjQIHkhQC8vL7Zs2cKgQYMYMWIE169fJ3v27NSoUeORiwVWrlyZSZMmMW3aNCIiIsiZMydt27Zl2LBhj6wnZ86cbN68mf79+9OtWzfi4uIoV64cy5Yte+pVkoW5/N39eaPYG7xR7A0WH1nMl5u+5P0V79N3dV9alGjBiJojyJ3F9uc+jx6tw02JErB2LWTPbnZFQoinJSsZC6sh3wfpzzAMtl/Yzo/bf2TOoTkYhkHFPBVpXLQxH1b8EDtlG73c0dGwaBFERurVh48dg0uX4NYqDEKIDERWMhZCPDelFMG5g5nZbCZHex7lg5c/IDw2nH6r+1F5YmUOXDlgdonP5eBB+Phj8PeHt96Czp3h6lUoXFjCjRDWTgKOECJFCngX4Ls633Gg2wF+bvgz+y7vo8z4Mrw44UXmHJzzVDvSZwRz5+rBwiNGQLlyMG+e7pbKls3syoQQqUECjhDiqXV+sTNHeh5hyCtDuBZ9jTfnvkmliZUyfItOeLie8g3QtCkMGQIXLsDmzfqxr6+59QkhUo8EHCHEM8mTNQ9Dqw/lxPsn+G+9//Lv1X+pNrkaP+/8mYSkBLPLe8D167p1JjgYtmzRG2AOHaqngQshbI8EHCHEc3Gwc6DnSz3Z8d4OCnkXouuyrlSZVIWDIQfNLi3ZhQtQujQkJsJPP+mQI4SwbRJwhBCporBvYXa8t4OfG/7MgSsHKPVTKbos6cK5iHOm1nXoEFSqpGdFff89dO0K9vamliSESAcScIQQqUYpRecXO3Pqg1N0C+rGhN0TKPRjId5d/C7Hrh8zpaarVyEmBnbtgt69TSlBCGECCThCiFSX3SM74xqM459u/9ChbAcm751MyXEl6bW8F7GJab+xalycXqDPMKBqVdizB154Ic3fVgiRgUjAEUKkmZL+Jfml0S8cf/84tQrVYsyOMbw29bU07baKioLGjeG112DhQj2YOFeuNHs7IUQGJQFHCJHm8nvlZ9lby5jRdAY7Luyg1E+lGLV1FBbDkqrvc/48BATAypXwxRfQpEmqXl4IYUUk4Jhk8uTJKKWSb05OThQqVIiBAwcSG5v2TfiPkz9/fjp27Jj8+Hatd+8WLsSzeKv0WxzodoAy2cvw4aoPqT65OmtOrkmVa1sskD+/3m5h9GgYODBVLiuEsFIScEw2Z84ctm7dyrJly6hTpw5fffUV/fr1M7ssIdJMUb+ibOy4kTH1xrD/yn5qTatFsTHFWH5s+XNd9/x5cHOD7t3h/fdTqVghhNWSgGOycuXKERwcTK1atRg3bhw1a9Zk4sSJWCyp23QvREailKLHSz04/+F5vqv9HRbDQsOZDXlv8XtPvUjgyZN6UHGePPDHHzB2bBoVLYSwKikKOEopH6XUAqVUlFLqjFLqrUec56yUGq+UuqKUClVKLVFKyfC+p/DCCy8QHR3NtWvXko+dOnWKNm3akC1bNpydnSlXrhwLFix44LX79u2jSZMm+Pr64urqStGiRfnqq6+Sn1+1ahX169cnR44cuLm5UapUKb777juSkpLS5bMJcT8PJw8+rPghuzrvokeFHvxvz/946X8vsf/K/hS9vlcvKFQIPvkElILKldO4YCGE1XBI4XljgXggO1AOWKaU2mcYxv1LlX4AVATKABHABOC/QNPUKfde/1nxH/Ze3psWl06xcgHl+KHuD6l2vdOnT5M1a1Z8b22Kc+7cOV5++WX8/f0ZNWoU2bJlY9asWTRr1oyFCxfSqFEjAP7++2+qV69OYGAgo0aNInfu3Bw7doz9++/8ojh58iSvvfYavXr1wsXFhZ07dzJ06FCuXr3KiBEjUu0zCPG0PJ09+W/9/xKcO5guS7vwws8vMLT6UAZUGYCD3cN/TH38MYwZA35+8MEH6VywECLDe2LAUUq5A82AUoZhRAKblVKLgXbAgPtOLwCsNAzjyq3XzgK+T92SbUtSUhKJiYncvHmTBQsWMG/ePH744Qfsby21OnToUAzDYMOGDcmhp06dOpw7d47BgwcnB5y+ffvi6+vLtm3bcHNzA6BGjRr3vFfXrl2T7xuGQdWqVYmPj+fbb7/lyy+/xM5OeiyFudqUaUNw7mB6/dGLT9d9yuIji5nUeBIl/UsmnxMfrxfsGzdOT/8+fBg8PEwsWgiRIaWkBacIkGgYxtG7ju0DXnnIub8Co5VSOYFwoA3wx8MuqpTqDHQGyJs379PUnCw1W07MUqxYsXsed+/enZ49eyY/XrFiBfXr1ydr1qwkJiYmH69Tpw79+vXjxo0bODg4sGXLFvr165ccbh7m0qVLDB06lBUrVnDx4sV7rhcSEkJAQEAqfjIhnk0hn0Ise2sZU/dNpecfPXn5fy+zpv0agnPrDaTCwmDBAujbF776ChxS2g4thMhUUvInuwdw475jEYDnQ849BpwDLtx6TXFg+MMuahjGBMMwggzDCMqWLVvKK7YxCxYsYMeOHSxfvpyaNWsybtw4pk6dmvx8SEgIU6dOxdHR8Z7b7ZlW169fJywsDIvFQu7cuR/5PhaLhUaNGrF06VIGDRrE2rVr2bFjB5988gmA6VPThbibUooO5Tqwu/NufN18eWXyK/SYM4y4hESyZ4eDB2HkSAk3QohHS8mPh0ggy33HsgA3H3LuWMAZ8AWigI/QLTgvP0eNNq1UqVIEBgYCukupTJky9OvXj2bNmuHu7o6vry9Vq1alf//+D319zpw5SUpKws7OjgsXLjzyfU6cOMHOnTuZNm0abdu2TT6+ZMmS1P1AQqSiwr6F2dV5F01/eZ9xh4ay/1Acm4Z8ibe32ZUJITK6lLTgHAUclFKF7zpWFrh/gDHoAciTDcMINQwjDj3A+CWllN/zl2r7nJ2dGTlyJCEhIYwbNw6AunXrsn//fkqWLElQUNADN2dnZ9zc3KhSpQrTp08nJibmodeOjo4GwNHRMflYQkICM2bMSPsPJsRzOLDdj7/7z8T7zNtsVd/w7V/fPvVUciFE5vPEgGMYRhQwHxiulHJXSlUGGgPTHnL6DqC9UiqrUsoR6A5cNAzj2kPOFQ/RqFEjKlSowHfffUdMTAzDhw8nIiKCatWqMWXKFDZs2MDChQv5/PPP6dSpU/Lrvv32W65fv07FihWZNm0a69at49dff6VXr14AFC9enHz58vHJJ58wd+5cFi1aRK1atcz6mEKkyK5dUKuWngq+6/MfqF2oNv1W96P8z+X59+q/ZpcnhMjAUjptpjvgCoQAvwHdDMM4qJSqqpSKvOu8vkAseizOVaA+ILvBPKXPP/+cK1euMH78ePLmzcvOnTspW7YsAwcOpFatWnTr1o0NGzbcM0uqQoUKbNmyhTx58tCrVy/q16/PyJEjk8flODk5sXDhQgICAmjfvj09evSgWrVqDBhw/0Q4ITKGxESoWxeSkmDmTCiQMwvL2yxnfIPxnIk4Q/Up1Zm0Z5LZZQohMihlGIbZNRAUFGTs3Lnzkc//+++/FC9ePB0rEhmRfB9kPpMnQ2AgVKly7/F9l/fx7pJ32XlxJ30r9uXrWl9jp2SZAyEyA6XULsMwgp50nvxEEEJkKMeOweuvg2FAx44PhhuAsgFl+avHbnWzAAAgAElEQVTTX3QL6sa3W7+l3ox6hMWEpXutQoiMSwKOECLDsFigTRsICdH3H8fR3pGx9ccytv5Y1pxcQ90ZdSXkCCGSScARQmQYvXvDjh3QoQPcWsz7sZRSdK/QndnNZ7P38l6qT6nO1airaV+oECLDk4AjhMgQJkyAH3/U3VLduj3da5uVaMbS1ks5cu0IdWfUJTQmNE1qFEJYD6sJOBlhMLQwj/z727abN2HgQL0b+IQJemfwp1WrUC1mt5jNnkt7aD67ORGxEalfqBDCalhFwHF0dHzkAnYic4iJiblnkUJhWzw9YeVK+OMPeJ5/5kZFG/FTg59Yd3od+Ufnl2nkQmRiVhFw/P39uXDhAtHR0fKXfCZjGAbR0dFcuHABf39/s8sRqez33+Gzz/SA4hdf1EHneXUJ6sK2d7ZR2r80nRZ34t3F75JkSXr+CwshrIpVrIMDcOPGDUJCQkhIkCXaMxtHR0f8/f3JkuX+LdGENTt/HooVg4IF4e+/wcUlda8fkxDDe0veY8aBGdQNrMvs5rPxdE6FBCWEMFVK18Gxmr14s2TJIr/ghLARMTF6rRs7O5g9O/XDDYCroyvTmkyjSt4qdF/WnYq/VmROizkUzyaLRQqRGVhFF5UQwnYkJkKRIrB3L/z2m27FSStKKboGdWVOizmERIVQZVIVlh1dlnZvKITIMCTgCCHS1YYNunvqm2+gQYP0ec9mJZqxut1q/N39afx7Y3478Fv6vLEQwjQScIQQ6apaNdi6Ffr1S9/3LRtQlq3vbKVK3iq0W9COlcdXpm8BQoh0JQFHCJEu/v0Xpk+HqCgIDjanBi8XL5a0XkKJbCVo9HsjlhxZYk4hQog0JwFHCJHm4uKgSRPo2RNCTV5k2NPZk1XtVlE2e1nemPUGcw7OMbcgIUSakIAjhEhTFgu88w4cOaIHFRcsaHZFEOARwOp2qwnOHUy7Be3YcnaL2SUJIVKZBBwhRJoxDOjVC2bMgC++gHr1zK7ojqwuWVnUahF5suah1rRarDu1zuyShBCpSAKOECLNrF4N48bpAcUff2x2NQ/yc/NjfYf1FPAuQMPfGvJPyD9mlySESCUScIQQaaZ2bThwAL7++tk20EwPubLkYlXbVXg6eVJrWi1m/TMLi2ExuywhxHOSgCOESHUREXpXcIBSpTJuuLktV5ZcrGm/BjdHN1rNa0Xrea1JtCSaXZYQ4jlIwBFCpKqkJGjdGnr31lPCrUUp/1Ic7XmUr177itkHZ9NjWQ/Z3FcIK2Y1e1EJIaxD377wxx8wfjy4u5tdzdOxt7NnQJUBRMRGMGLLCF7K9RLvvPCO2WUJIZ6BtOAIIVKFxQJdu8IPP+iZU126mF3Rs/u8xufULFiTnn/0ZPel3WaXI4R4BhJwhBCpYvVq+PlnePtt+O47s6t5PvZ29kxvMh0/Nz9qTKnBkWtHzC5JCPGUJOAIIVJFnTp6I81ffwVHR7OreX7ZPbKz+e3NONg50Gx2MyLjI80uSQjxFCTgCCGey7ffwqpV+n61ahl/xtTTyOeVjzH1x3Dw6kFazGlBkiXJ7JKEECkkAUcI8cz27tWL+I0ZY3YlaadVqVb8WPdHVhxfQet5rYlOiDa7JCFECsgsKiHEM4mN1eNtQM+YsmW9Xu5FaEwoQzcM5eLNi6zvuB4HO/nxKURGJi04QoinZhhQuLBuwVm0CHLmNLuitDek+hAmNprIlnNbaDCzAQlJCWaXJIR4DAk4QointmQJnD8P7dpBo0ZmV5N+3i7/NmPrj2XViVW0nNuS2MRYs0sSQjyCBBwhxFN7/XVYsQImTTK7kvTXvUJ3RtUZxYLDC2g6q6ls6SBEBiUBRwiRYpcuwe7deqZUnTpgb292Reb4T/B/GFVnFH8c/4MWc1rIwGMhMiAJOEKIFImLg5o1oW5d69pjKq38J/g/fF/7exYdXiRjcoTIgCTgCCFSJEcOOHRITwm3tj2m0krvir355fVfWH96PbWn15bFAIXIQCTgCCGeaPp0CAvT08LffNPsajKWTuU78UOdH9h4ZqMMPBYiA0lRwFFK+SilFiilopRSZ5RSbz3m3BeUUhuVUpFKqStKqQ9Sr1whRHq7eFHPlgoMtP31bp6FUooPgj9gbP2xLD+2nO7LusvAYyEygJSuVDUWiAeyA+WAZUqpfYZhHLz7JKWUH7AC6A3MBZyA3KlXrhAivWXPDo0b6y0ZnJzMribj6hrUlZNhJxn510iiE6KZ2WwmdkoayYUwyxMDjlLKHWgGlDIMIxLYrJRaDLQDBtx3+ofASsMwZtx6HAf8m4r1CiHS0bVr4OcHCxeaXYl1+KbWN3i7eDNw7UACPAIYVWcUypY25xLCiqTkz4siQKJhGEfvOrYPKPmQc4OBUKXUX0qpEKXUEqVU3oddVCnVWSm1Uym18+rVq09fuRAiTU2bprulDhwwuxLrMqDKAD54+QNGbx/NwD8Hml2OEJlWSgKOB3DjvmMRgOdDzs0NdAA+APICp4DfHnZRwzAmGIYRZBhGULZs2VJesRAizW3bBu3bQ+nSULy42dVYF6UUo+qMokPZDozYMoKp+6aaXZIQmVJKxuBEAlnuO5YFuPmQc2OABYZh7ABQSg0DrimlshqGEfFclQoh0kVCwp1NNKdMAQfZU/KpKaUYU38MR68fpcPCDmRxzsIbxd4wuywhMpWUtOAcBRyUUoXvOlYWOPiQc/cDxl2PjYecI4TIoAwDPvgADh+GWbOgYEGzK7JeHk4e/Nn+T0pkK0H7Be1ZcXyF2SUJkak8MeAYhhEFzAeGK6XclVKVgcbAtIecPgloopQqp5RyBD4FNkvrjRDWwWKB6Gjo00fWu0kNro6uLG29lNxZctNkVhOWHFlidklCZBopncPYHXAFQtBjaroZhnFQKVVVKZW8dKdhGGuBgcCyW+cGAo9cM0cIkXEYht5batIk+OYbs6uxHQW8C/Bn+z8p5leMt+a/xd7Le80uSYhMQRmG+b1IQUFBxs6dO80uQ4hM68QJaNgQFi2CIkXMrsY2nYs4R+WJlYlJjOGvTn9R2Lfwk18khHiAUmqXYRhBTzpPVqESIpOzWKBRIz3uJrPuDp4e8mTNw6p2qzAMg0oTK7HzovxRJ0RakoAjRCb3+ed6E81PP4VChcyuxrYV8yvGtne34eHkQdNZTQmNCTW7JCFslgQcITKxDRtgyBBo0waGDTO7mswh0CeQGU1ncCXqCg1nNiQqPsrskoSwSRJwhMjEBtzabOW//wXZUSD9VMpTid+a/cb2C9tpOrupbM4pRBqQgCNEJrZxIxw/Dt7eZleS+TQt3pTRdUez6sQqhm8YbnY5QtgcCThCZELjxsHly+DoKONuzNSjQg86lO3AZxs/Y/GRxWaXI4RNkYAjRCazaxf06gVjx5pdiVBKMb7heF7M8SJvznmTBf8uMLskIWyGBBwhMpG4OOjQAQIC9GrFwnwuDi4sab2EMtnL0GZ+G/Zc2mN2SULYBAk4QmQiffrAwYMwYQJ4eZldjbgth2cOlrRegq+bL41/b8yVyCtmlySE1ZOAI0QmMXGi7pbq3RsaNDC7GnG/7B7ZWdxqMdeir9F0dlPiEuPMLkkIqyYBR4hMom5d+OwzGDnS7ErEo5TPUZ4pb0zhr3N/0WN5DzLCVjpCWCsJOELYuJgYiI2FnDlh0CDZjiGja1GyBR9X+Zhf9/zKz7t+NrscIayWBBwhbFh8PLz5pm69SUoyuxqRUp/X+Jy6gXXp9Ucvtp7banY5QlglCThC2KikJD1jaulSHXKk5cZ62Ck7fmv2GwEeATSY2YBNZzaZXZIQVkcCjhA2yDD0HlO//w4jRkD37mZXJJ6Wl4sXGztuxN/dnzdmvcGlm5fMLkkIqyIBRwgb9N138MUX0KkTfPSR2dWIZ1XAuwDz3pxHXGIczWY3w2JYzC5JCKshAUcIG1S7Nrz/vl7vRjbRtG4l/UvyU4Of2Hp+Kx+u/NDscoSwGhJwhLAhR4/q7qkyZWD0aBl3YyvalmlL96DujN4+mnE7xpldjhBWQQKOEDbi0CF44QX48UezKxGpTSnF6HqjaVC4AR+s+IA5B+eYXZIQGZ4EHCFsQHg4tGgBbm76q7A9DnYOzGg6g/IB5emwsAP/hPxjdklCZGgScISwcnFxehr4v//ClCl6QT9hm7K6ZGVhq4V4uXjxxu9vEJ0QbXZJQmRYEnCEsGKGAR07wurVMH481KtndkUireX0zMnMZjM5EXaCIeuGmF2OEBmWBBwhrJhS0KiRnhbeubPZ1Yj0Uj1/dbq82IXvt33P7IOzzS5HiAzJwewChBDPJiwMvL2hdWuzKxFm+Lrm1+y5vIdWc1tx/sZ5PqwoU8iFuJu04Ahhhf7+G/Llg2XLzK5EmCWrS1bWd1hPgyIN6LOqDyuPrzS7JCEyFAk4QliZy5ehWTPw9YXgYLOrEWZydXRlZtOZFPMrRtdlXWXQsRB3kYAjhBUJDdWrFIeFwbx5OuSIzM3T2ZOfG/7M6fDTdFnahYSkBLNLEiJDkIAjhJWIioIGDeDIEVi4UC/qJwRAtXzVGPrKUKbvn06P5T3MLkeIDEECjhBWwt4eKlXSO4TXrGl2NSKjGVJ9CJ1f6Mwvu39h4p6JZpcjhOlkFpUQViAiArJm1dPBhXiUMfXHcCr8FF2WdiGXZy7qBNYxuyQhTCMtOEJkcD/8AKVKwblzZlciMjpHe0fmvjmXktlK0nxOc9nOQWRqEnCEyMCmTIHeveGll2QLBpEyWZyzsKT1ElwcXGg+uzknQk+YXZIQppCAI0QGNXUqdOoEhQvD9Ol6DI4QKZEnax7mvTmPizcvUm1yNS7evGh2SUKkOwk4QmRAkyZBhw5QvTrs3g2urmZXJKxNtXzV2PT2JiJiI6g6qSrnIqSPU2QuEnCEyICaNIHhw+GPP8DDw+xqhLUqG1CW1e1Wc/7GeWpNq8XZiLNmlyREuklRwFFK+SilFiilopRSZ5RSbz3hfCel1L9KqfOpU6YQmcOqVRATA15e8Omn4ORkdkXC2lXMU5Hlby3nUuQlqk2qxoErB8wuSYh0kdIWnLFAPJAdaAP8pJQq+Zjz+wFXn7M2ITKViROhTh0YOtTsSoStea3ga6xut5rQmFDenPsm4bHhZpckRJp7YsBRSrkDzYBPDcOINAxjM7AYaPeI8wsAbYGvUrNQIWzZjBnw7rs64AwfbnY1wha9lOslpjWZxonQEzT6rRGGYZhdkhBpKiUtOEWARMMwjt51bB/wqBac/wIDgZjHXVQp1VkptVMptfPqVWnsEZnXnDnQvr0eUDx/Pjg7m12RsFWNizXm29rfsunsJmYdnGV2OUKkqZQEHA/gxn3HIgDP+09USjUB7A3DWPCkixqGMcEwjCDDMIKyZcuWomKFsDU3b0K3blCxIixeDG5uZlckbF23oG6UCyjHO4vf4e8Lf5tdjhBpJiUBJxLIct+xLMDNuw/c6sr6Bng/dUoTwvZ5esKff8Ly5TJbSqQPR3tHlr21DF9XX1rNbUVoTKjZJQmRJlIScI4CDkqpwncdKwscvO+8wkB+YJNS6jIwH8ihlLqslMr//KUKYTvWr4fvv9f3y5aFLPf/CSFEGsrpmZPZLWZz4eYFWs5tSaIl0eyShEh1Tww4hmFEocPKcKWUu1KqMtAYmHbfqf8AeYByt27vAldu3ZcVpoS4Zc0aqF8f/vc/PSVcCDME5w7mpwY/sebkGprNbiYhR9iclE4T7w64AiHAb0A3wzAOKqWqKqUiAQzDSDQM4/LtGxAKWG49TkqT6oWwMsuXQ8OGEBioW3FkhWJhpk7lO/FFjS9YfGQx7Ra0k5AjbIpDSk4yDCMUeOMhxzehByE/7DXrgdzPU5wQtmTRImjRAkqX1gv6+fqaXZEQMLDqQCyGhU/XfUpp/9IMrDrQ7JKESBWyVYMQ6eT4cXjhBT2oWMKNyEgGVRvEG8XeYMTmERy6esjscoRIFRJwhEhj0dH6a58+sHGj3oZBiIxmZK2RuDm6UenXSqw+sdrscoR4bhJwhEhD8+dDgQKwc6d+LHtLiYwq0CeQv9/7m7xZ89Lwt4YsO7rM7JKEeC4ScIRIA4YBY8boMTeFCkHBgmZXJMST5c2alw0dN1DavzRNZzdl/r/zzS5JiGcmAUeIVGYY0Lcv9Oqlp4OvXg0+PmZXJUTKeLt6s6rdKvJkyUOz2c1YeHih2SUJ8Uwk4AiRyiZP1ov49eypZ065u5tdkRBPx8fVh91ddlMhZwVazm0pLTnCKknAESKVtWoFY8fCjz+CnfwfJqxUFucsrGy7kmJ+xWg5tyUrj680uyQhnor8+BUiFRw/Dk2bQkSEXryve3dQyuyqhHg+3q7erG2/luJ+xWkwswFzDs4xuyQhUkwCjhDPadkyqFABNmzQQUcIW+Lr5suGjhsoG1CWtgvasvPiTrNLEiJFJOAI8YwSE6FzZ731Qt68eir4iy+aXZUQqc/b1ZuVbVeS3T07b/z+BtvObzO7JCGeSAKOEM+oRw/45RcdcrZv1+vdCGGr/Nz8WNx6MXbKjoYzG3Iq7JTZJQnxWBJwhHhKSbe2jv3oI5gyBX7+GVxczK1JiPRQLqAca9qvIcGSQPuF7YmKjzK7JCEeSQKOEClkscDAgdCypb5fqBC0b292VUKkryK+Rfi+9vdsPruZJrOaEJsYa3ZJQjyUBBwhUiA6Wk///uorvWjf7VYcITKjd154h58a/MTqk6tpMqsJcYlxZpckxAMk4AjxBAcPwksvwdy58N13ukvK0dHsqoQwV9egroxvMJ4Vx1fQfmF7DMMwuyQh7uFgdgFCZGTx8VCrlm6xWbECatc2uyIhMo4uQV0IiQph8PrBdCzbkXqF65ldkhDJpAVHiIcID9fjbJycYOZM2L9fwo0QD/NR5Y8o5leM5nOaM+/QPLPLESKZBBwh7rNsGRQpordaAKheHbJnN7UkITIsZwdn1rZfS5nsZWg+pzmD1w3GYljMLksICThC3HbtGrz7rl64L0cOeOUVsysSwjrk8MzBug7r6FiuI59t/IyP13wsY3KE6WQMjhDA779D69bg4AB9+8Lnn4Ozs9lVCWE9XBxcmNhoIo52jnzz1zdcibrC/xr9Dwc7+TUjzCHfeUIAN25AiRI66JQubXY1QlgnpRQ/N/yZXJ65GLphKC4OLoytPxZ7O3uzSxOZkAQckSlZLHoV4gsXYNAgeO893T1lJ522QjwXpRRDqg8hOiGab/76Bk8nT0bUHCEhR6Q7+XEuMp2//4YqVaBTJ1i9Wm+aqZSEGyFS09e1vqZ92fZ8u/Vb3l70NkkWWR1TpC/5kS4yjbAwvTHmyy/D8eMweTKsW6fH3QghUt/kxpMZXn040/ZPo8WcFiRaEs0uSWQiEnBEpnHunA41ffvCiRPQoYO02giRlpRSDKo2iJG1RrLg8AK6LOkiLTki3cjfrsKmbdkCa9bAkCFQpgycPg05c5pdlRCZh1KKvpX6ciPuBp9t/IyohCimNpmKk72T2aUJGyd/vwqbdO4ctGunx9r8738QEaGPS7gRwhzDXx3ONzW/YdbBWdSdXperUVfNLknYOAk4wqbcvAmffKJXIp4zBz7+GA4fhqxZza5MCNGvcj8mNZ7E1vNbeel/L3H0+lGzSxI2TAKOsCnh4XrH76ZN4ehR+PJLcHc3uyohxG0dy3VkY8eNXIu+xosTXmTwusHcjLtpdlnCBknAEVYtIQHGjoU2bfTjPHn0AOIZMyBvXnNrE0I8XIVcFfj73b+pU6gOn238jKqTqnLx5kWzyxI2RgKOsEqxsfDTT1C4MPTsCefP6+4pgFy5zK1NCPFkxbMVZ+6bc1ncajFHrx+l1rRahESFmF2WsCEScITV+esvKFgQunfXg4aXL4f168HT0+zKhBBP6/Wir7O8zXJOhZ2ixpQanAo7ZXZJwkZIwBFW4eZNPaYGoGhReOEFWLtWTwOvV0+vRCyEsE7V81dn6VtLORV+iqJjirL4yGKzSxI2QAKOyNCOHYP339fdTm+9BYYBvr6wdCm8+qoEGyFsRY0CNdjdeTf5vPLR+PfGNJvdjNPhp80uS1ixFAUcpZSPUmqBUipKKXVGKfXWI87rp5T6Ryl1Uyl1SinVL3XLFZnF1q3QpIlurRk/Hho31mNuJNAIYbuK+hVlX9d9DKs+jKVHl1JyXEmm7J2CYRhmlyasUEpbcMYC8UB2oA3wk1Kq5EPOU0B7wBuoC/RUSrVKjUKF7YuIgLNn9f2//oLNm2HAAH1s2jSoUMHc+oQQac/N0Y3BrwxmT5c9lMhWgo6LOtJ1aVcshsXs0oSVeWLAUUq5A82ATw3DiDQMYzOwGGh3/7mGYXxjGMZuwzASDcM4AiwCKqd20cK27NsHvXqBlxf8/rs+1q2bDjZffgkBAebWJ4RIfyWylWD7u9v5uMrHTNg9gfcWvyf7WImnkpK9qIoAiYZh3L3k5D7glce9SCmlgKrAz494vjPQGSCvLFiS6SQkwJgxMH067N4NTk56cb5KlfTzbm7m1ieEMJ+dsuOLGl/gaOfI8I3D2XxuM6PrjqZuYF2zSxNWICVdVB7AjfuORQBPmpQ79Nb1Jz3sScMwJhiGEWQYRlC2bNlSUIawdvHxsHevvu/gcGdMzQ8/wKVLMG+e3jtKCCFuU0ox7NVhTGw0EcMwqDejHq3mtuJy5GWzSxMZXEoCTiSQ5b5jWYBHrq2tlOqJHovTwDCMuGcvT1i7hARYvRq6dtUzoV59FeLidLD5+2/YuRM++AB8fMyuVAiRkb1d/m0OdDvA8OrDWXB4AcXGFGPWP7PMLktkYCkJOEcBB6VU4buOlQUOPuxkpVQnYADwmmEY55+/RGGtJk2C7Nmhdm09SLhGDb2FgsOtjlEvL3PrE0JYF2cHZz595VP2d91PMb9itJrXig9XfsiNuPs7GYRIQcAxDCMKmA8MV0q5K6UqA42Bafefq5RqA3wJ1DIM42RqFysyrrAwPUC4TRs9aBj0XlANGsCCBXDtGsyaBfXrg729ubUKIaxbUb+ibHx7I92CujFq2ygKji5I/9X9uRZ9zezSRAaiUrK+gFLKB5gI1AKuAwMMw5iplKoK/GEYhset804BuYG7u6WmG4bR9XHXDwoKMnbu3PmMH0GYJSwMfvkFli3TKwonJYGfH/z8sx4wLIQQae2vc38xfMNw/jz1J94u3gyoMoAuL3bB3cnd7NJEGlFK7TIMI+iJ52WEBZQk4FiH6GhYs0bPeKpbVwccf38oUQJef123zrz8srTQCCHS3/bz2+m/pj8bzmygoHdBFrZcSOnspc0uS6QBCTgiVezfD6tWwcqVsGmTHiBcpw6sWKGfv3ZNt9oIIYTZDMNgxfEVdFrcifDYcL6v/T1dg7qiZAl0m5LSgCN7UYlkSUlw4ADMn3/n2HvvQb9+ehp3jx467Cy+ax88CTdCiIxCKUW9wvXY02UP1fNXp/vy7lSbXI1j14+ZXZowgbTgZHL79+sxNBs36u0RbtwAZ2cIDwcXF9izB7Jlg9y5za5UCCFSzmJY+GXXL/Rb3Y8kI4lx9cfRtkxb7O2kD93aSQuOuIdhwKlT8NtveluE6Gh9/PffYeBAOHcOWreGqVPh0CEdcgDKl5dwI4SwPnbKji5BXTjc8zBls5el46KO+H/rz/dbvycuUZZnywykBccGGYa+2dnplplvv4UdO+DyrYU/3d31bt2lS8OVK3pQsHQ1CSFsVZIlifn/zmfsjrFsOLOBQJ9APnv1M1qWbCnjc6yQtOBkEjExekXgyZP1WJn69SFHDr16MEBsLJw4ATVrwtixet+n8HAdbkAvxCfhRghhy+zt7GlRsgXrOqxjaeulONk70Xpea9rMbyNbPtgwacGxAvHxunvp+HF9O3YMmjeH6tX1+jO3929ydoaiRaFsWb39wYsvmlq2EEJkSEmWJIZvGM6Xm7/ExcGF9mXa81Hlj8jnlc/s0kQKyDRxKxMTA6dP69aWY8d0OKlWDY4eheLFwWK5c26WLDByJHTuDJGR8Oefei2aAgXubIMghBDi8Y5eP8rgdYOZe2guSim6vtiVvpX6StDJ4CTgZDBRUXD27J1bYKDeePL6dShV6s74mNv694cRI3QX01df6fMDA6FQIT2rSbqNhRAidZyLOMeXm77kl92/kGQk0Tu4N5+9+pmshpxBScBJJ4ahQ8qlS3Dxor5duKC7ilq00N1LuXLpBfHu1qkT/Pqrfn3XrpAnD+TPr0NM4cJ6d20JMUIIkX5Ohp3k681fM2H3BNwd3RlWfRj/Cf6PTC3PYCTgPIfERN1l5OmpH8+bpwNMSIiedXTpku5CGjJEBxR3d33+3Tp00AN/Afr00QN58+XTG1DmyaNDj3QnCSFExrPxzEa+2vwVK46vIF/WfHQL6kaPl3rg4eRhdmkCCTgYxp0WkAMHdAtKWJi+hYdDzpx63ReAVq3g5EkIDdWtMeHh0KgRLFqkn8+RQ3chKaW7h3Lk0LOVvvxSPz9pkg45OXPeubm4pOrHEUIIkY4shoX5/87n+63fs/X8Vvzd/eke1J2O5TrKGB2TWXXASUjQex553ArLu3frgHLzpr7duKFbRN56Sz/ftasejBsefifAvPzynf2SChTQA3jvVqOGHpwLekZSVJTuFvL21iGmdOk7O2IfP64H9vr6ykaSQgiR2Ww9t5XPNn7GH8f/AOD1Iq/zcZWPqZinosmVZU5WFXC8vIIMX9+d3LihA0xcnJ76vGmTfr54cTh8+N7XVKyotxYAHUSuXgUvLx1QvLz0wN3OnfXza9fqRe/uft7TUx8TQgghUuJk2Ekm7ZnEqG2jiEqIIl/WfNQvXJ9WpVpRLV81s8vLNKwq4GTPHmTUqrUTT0+SbwULQps2+vm//tLTpG8/lzWrblFxdDS3biGEEJlPVHwUU/ZNYc3JNaw4voKYxBjeKPYG39X+joLeBW2szzIAAA63SURBVM0uz+ZZVcDJaIOMhRBCiJSIjI+kz8o+TNo7iURLIjUK1KBjuY40Ld4UN0c3s8uzSRJwhBBCiHRyNuIsk/dOZvLeyZwKP4WnkyctS7akY7mOBOcOlqnmqUgCjhBCCJHOLIaFTWc2MWnvJOYcmkN0QjS+rr68U/4dulfoLjOwUoEEHCGEEMJEN+NusuToEub/O58FhxfgaOfIyFoj6VahGw52shDas5KAI4QQQmQQp8NP02lRJ9adXkdB74IMqDyAtmXa4uroanZpVielAUcmSgshhBBpLL9Xfv5s/ycLWy7Ex9WHzks74/W1F9UmVWPwusHsurjL7BJtjrTgCCGEEOnIMAzWn17P8mPL2XBmAzsv7sTAoGz2stQLrEen8p0o7FvY7DIzLOmiEkIIIaxASFQIk/ZMYvnx5Ww8sxGAuoF16V+5P6/kewUlOy/fQwKOEEIIYWWOXj/6//buP0qq8r7j+PvDr12Z3VUW2F1ARCQLVpIjGM9JQERNk2iUoCm0NbY98aQ9njb1jxzS0yY9aWo1rcZWc9rY5scJiUYhJNoQkwOWkIAIVWqaCrEoCIssgvx03V3YhQXh6R/3Lp0dZnbuxJ2d3dnP65x7YO7zPDPPPPPlzpd7n7kPy369jEd++QgtJ1q4euLVLPngEhbOWEhqVKrU3RsQnOCYmZkNUu1d7Ty65VEe/M8H2X9sP8M1nLmT57L4isUsnLGQKRdOGbJndpzgmJmZDXJnzp7huebnWL1zNU++8iTNbc0A1F5Qy5yL57Bg+gI+cfknqK+qL3FP+48THDMzszKz4+gO1jStYcvBLfz0tZ9ytPMoF1ZcyG2X38bcyXOZ3TCbqyZcVdZ3Tk6a4PhOQ2ZmZoPEjHEzmDFuBhDdNXnrwa18eeOXWbVzFY9tfQyAqlFV3HDpDXxg0gdYOGMhjWMbqRxRWcpul4TP4JiZmQ1yIQR2v72bF/e/yIbmDSx7eRnHTx0/V37JhZfw6Vmf5uMzPs7shtmDev6OL1GZmZkNUSEEth/dzpaDW1j3+jq2HdnG5n2bCQQmVk9k+tjpTKiaQENVA9PHTmfeJfOYOX7moEh8nOCYmZnZOYc7DrN652rW7l7L3ra9HDh2gL1tezl99jQQ3W35fXXv4+Kai5l60VSmjpnKzY03M3rk6BL3vCcnOGZmZtars+EsTS1NrHt9HWt3r2VXyy6a25ppPdl6rs4V46+gsbaRqRdNZezosYypHENDVQOXjbmM99S+h+qK6n7tsxMcMzMz+420d7Xz7J5neXH/i7x8+GV2texiT+seOk939qg3YtiIc4nOtDHTqE/VM3vCbBprG5lWO41h6vslL53gmJmZWZ86deYUrSdbeaPtDfa07uFXB37FzpadNLU08dLBl3rUvWDEBdRX1VOXqqOhqoHJNZOpqaihelQ1NRU11FTUMD41nrpUHamRKVKjUuf298Y/EzczM7M+NWr4KOpSddSl6nj/xPez6IpF58rOhrPn5vW8evRVXjnyCoc6DnG44zBNLU1s2ruJ9q523jn7Tq+vkRqZorqiukcy1P24bnRd4r4mSnAk1QJLgY8CR4EvhBCWZ6kn4AHgT+Jd3wY+HwbCaSIzMzMrmmEaxqSaSUyqmcScyXOy1gkh0HWmi/audtpOtnG44zBHOo/QcaqDztOdvHXiLY50HOHYqWO0d7Wf+7O5tZm2rjYOHT+UuD9Jz+D8K3AKqAdmAaskbQ0hbMuodxdwG3AlEIC1wOvANxL3yMzMzMqSJCpHVFI5opK6VB2NYxsLah9CYNgXk83ryVtLUgpYBPxNCOF4CGET8BPgj7JU/xTwUAhhXwhhP/AQcGfSjpuZmZnlUsh9epKcwZkOvBNCeC1t31bguix1Z8Zl6fVmZntSSXcRnfEBOC5pR4K+9LdxRJfkLD+PVXIeq+Q8Vsl5rJLzWCU3EMdqSpJKSRKcKqA9Y18bkO2H71VxWXq9KknKnIcTQvgW8K0knSwVSf+dZKa2eawK4bFKzmOVnMcqOY9VcoN5rJJcyDoOZP5mqwY4lqBuDXDck4zNzMysPyVJcF4DRkhKnwl0JZA5wZh435UJ6pmZmZkVTd4EJ4TQAfwIuFdSStI1wK3A41mqfw9YImmSpInA54BH+7C//W1AX0IbYDxWyXmskvNYJeexSs5jldygHatEdzKO74PzHeAjwFtE97ZZLula4JkQQlVcT8BX6HkfnL/yJSozMzPrTwNiqQYzMzOzvtT3q2CZmZmZlZgTHDMzMys7ZZvgSLpd0quSOiQ1xfOFust+W9J2SZ2S1kuaklZWIek7ktolHZS0JON5i9K21CQ1Sjop6YmM/XdIao7H8cfxfKzuslpJK+OyZkl39EfbUog/26Vxn45J2iLpYxl1HFd9JF98DGb5YslxlF22Y5SPT+dTju++IRlXIYSy24gmQzcDHyRK4iYBk+KycUQ3IPxdoBL4R2BzWtv7gY3AGOC3gIPATcVuW+oN+Fnc9yfS9s0kut/RfKKbOC4HVqSVfx/4QVw2L35/M4vdtkTjkwLuAS6NY2pB3MdLHVdFGe+c8THYt95iyXHU67j1OEYV8xjzbtqWeIyyfvcN1bgq+QdSpA/5eeCPc5TdBTyf9jgFnAAujx+/CXw0rfy+7uAtZtsSj9ftwA+JDrrpCc4/AMvTHk8jWnS1Ou7/KWB6WvnjwAPFbFvqscoYt18DixxXfT6uvcZHOW7dseQ4yjk+5x2jfHzKOk5Zv/uGalyV3SUqScOBq4HxknZJ2ifpEUkXxFV6rJcVovv8NAEzJY0BJpB7Pa2itH137/jdkVQD3AssyVKc2ecm4n/45F6jLNf77au2A4KkeqL+dN/I0nHVd/LFR1nJiCXHUYZejlE+PqXJ8903JOOq7BIcoB4YCSwGrgVmAbOBL8blmetlwf+vrVWV9jizrJhtS+k+YGkIYV+Wsnzvt7c1yorVtuQkjQSWAY+FELbHux1XfaeQ9e8GtSyx5Dg6X65jlI9PPfX23Tck42rQJTiSnpUUcmybiE5/AXwthHAghHAUeBi4Od7f29pax9MeZ5YVs21R5BsrSbOADwNfzfEU+d5vb++nWG2LIkFcddcbRnS6+hRwd9pTDJm46gfl+J7OkyOWHEdp8hyjhszxKaHevvuGZFwNugQnhHB9CEE5tnkhhLeBfUD6HQzT/95jvSxJKaJrqNvitgfIvZ5WUdoWPAgJ5Rsr4HqiiY17JR0E/gJYJOl/cvT5MqCCaH2yfGuUFattUSQYq+47dS8l+p/SohDC6bSnGDJx1Q8KWf9uUOollhxHPV1P7mPUkDk+JZHnu29oxlV/TPTp743oeu0vgTqimd0bgfvisvFEp8gWEc3q/go9Z4Q/AGyI211O9OHdVOy2JRqn0UBD2vZPwFPA+Lh8JtGp2muJJoc9Qc9fGqwg+rVBCriG839pUJS2JRyvbwCbgaosZY6rvh3rnPFRDluuWHIcnTdOOY9RPj5lHa+s331DNa5K/oEU6UMeCfwb0Er0k7V/ASrTyj8MbCc6pfcs8U9947IKonW32oFDwJKM5y5K24GwkfErqnjfHcBeoAN4GqhNK6sFfhyX7QXu6I+2JRqbKUT/GzpJdNq1e/sDx1VRxrvX+BjMW75Ychz1OnY9jlE+Pp03Pjm/+4ZiXHktKjMzMys7g24OjpmZmVk+TnDMzMys7DjBMTMzs7LjBMfMzMzKjhMcMzMzKztOcMzMzKzsOMExG8J6WZ4ifdsT131UUrY1y/qdpHvivo3oy+dLUO/6+HWv74vXNbPi6ZODg5kNWnMyHq8kWv33nrR9Xf3WGzOzPuIEx2wICyFsTn8sqQs4mrn/3ZJUEUJwomRm/caXqMysIJJmS9ooqVPSTkl/mlF+Z3wZZ76kJyW1Av+VVn6dpF9IOiapQ9IaSe/NeI4bJT0vqU3ScUk7JH0pS3emSloV12mW9KV4le7055ohaaWkVkknJG2WdFOC9zle0nJJ7XHb7wEXFTRYZlYyTnDMrBA1wHKiBQZvJVrY7+uSbshSdxnwOrAY+DyApFuAXxCtvfSHRGv6VAMbJU2O61wG/CRu+/vAQuBhooUNM60E1gG3Ea0d9HfAp7oLJU0ENhGtaHw38HtE6/SskvSxPO/1R8AC4K/jfrwDfC1PGzMbIHyJyswKUQ18JoSwHkDSc8CNwCeB9Rl1nwoh/GXGvn8GNoQQbu3eIWk9sBv4HPBZ4CpgFPBnIYT2uNq6HP15KITw3fjvP5f0obgv3fuWEK1yPCeEsCt+vdXAK8DfA89ke1JJHwHmAZ8MIayId6+R9AxwcY6+mNkA4jM4ZlaIzu7kBiCeV/MacEmWuivTH0hqBKYByySN6N6ATuAFYH5cdQtwGlghabGkul76syrj8f9m9GU+sLk7uYn7fAb4PjBLUk2O550DnAH+PWP/iix1zWwAcoJjZoV4O8u+LqAyy/4DGY+7E5WlRAlM+rYAGAsQJyM3Eh2fHgcOxvNmrsvyGi15+lKbpR8ABwERnd3JZgLwdgjhdMb+Qznqm9kA40tUZlYsmfeVeSv+8wvAz7PUP3WuYXSWaL2kCuAa4F6ieTOXhhCOFtCHFqAhy/6GuH/ZEjaIkqIxkkZmJDn1Bby2mZWQExwz6y87gD3AzBDCA0kaxJfA1kmqAp4GpgKFJDgbgM/GidEeAEnDiSYNv5Q2xyfTC8BwYBE9L0vdXsBrm1kJOcExs34RQgiS/hx4WtIo4IdEyUo9MBfYG0J4OP7Z+XxgNfAGMI7orM+bRHNsCvFV4E5graS/BdqBzwDTgVt66etaSZuAb0oaB+wkSorem6uNmQ0snoNjZv0mhLCaKHlJAd8G1gAPEl0yeiGutjUuvx/4GfAI0U/GPxRCOFHg671J9GuobcDXgaeI5uXcEkL4jzzNf4coybof+AHRfwjvLuT1zax0FELe5VfMzMzMBhWfwTEzM7Oy4wTHzMzMyo4THDMzMys7TnDMzMys7DjBMTMzs7LjBMfMzMzKjhMcMzMzKztOcMzMzKzs/B/gJEkLjt7L5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22139a52358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(precisions,recalls,thresholds):\n",
    "    plt.plot(thresholds,\n",
    "             precisions[:-1],\n",
    "            \"b--\",\n",
    "            label=\"Precision\")\n",
    "    \n",
    "    plt.plot(thresholds,\n",
    "             recalls[:-1],\n",
    "            \"g-\",\n",
    "            label=\"Recall\")\n",
    "    plt.xlabel(\"Threshold\",fontsize=16)\n",
    "    plt.legend(loc=\"upper left\",fontsize=16)\n",
    "    plt.ylim([0,1])\n",
    "    \n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions,recalls,thresholds)\n",
    "plt.xlim([-700000, 700000])\n",
    "save_fig(\"precision_recall_vs_threshold_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在您可以选择为您的任务提供了最佳的精确度/召回权衡的阈值。 选择良好的精度/召回权衡的另一种方法是直接绘制精确度以进行召回，如图3-5所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure precision_vs_recall_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22139a5f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, \n",
    "             precisions, \n",
    "             \"b-\", \n",
    "             linewidth=2)\n",
    "    \n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可以看到，在召回率为80%时，精度确实开始急剧下降。您可能希望在该下降之前选择精度/召回权衡 - 例如，召回率约为60％。 但当然，选择取决于您的项目。\n",
    "\n",
    "所以我们假设你决定瞄准90％的精度。 查看上一个图发现你需要使用大约70,000的阈值。 为了做预测（现在是在训练集上），如果不调用分类器的predict（）\n",
    "方法，你可以运行这段代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores > 70000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们检查这些预测的精确度和召回率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8659205116491548"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6993174691016417"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5,y_train_pred_90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "太棒了，你有一个精度为90％的分类器（或足够接近）！正如您所看到的，创建一个几乎任何您想要的精度的分类器相当容易：只需设置足够高的阈值，就可以了。 嗯，没那么快。 如果召回率太低，高精度分类器就不是很有用了！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 ROC curves\n",
    "**receiver operating characteristic (ROC)** 曲线是二元分类器的另一种常用工具。\n",
    "* 它与精确度/召回曲线非常相似，但ROC曲线不是绘制精确度与召回率，而是绘制**true positive rate(TPR)** （召回的另一个名称）与**false positive rate(FPR)** \n",
    "* FPR是负面例子被错误归类为正面例子的比例。它等于**1 - true negative rate（TNR）**，即负面例子被正确归类为负面例子的比例。\n",
    "* TNR也称为**specificity**。 \n",
    "\n",
    "因此，ROC曲线绘制了**sensitivity**（召回）与 **1 – specificity** 。\n",
    "\n",
    "要绘制ROC曲线，首先需要使用**roc_curve（）**函数计算各种阈值的**TPR和FPR**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure roc_curve_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213aabfbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里也有一个权衡：召回（TPR）越高，分类器产生的误报（FPR）越多。 **虚线表示纯随机分类器的ROC曲线**; 一个好的分类器尽可能远离该线（朝左上角）。\n",
    "\n",
    "比较分类器的一种方法是测量曲线下面积（AUC）。完美分类器的ROC AUC**等于1**，而纯随机分类器的ROC AUC**等于0.5**。 Scikit-Learn提供了计算ROC AUC的函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9624496555967156"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据经验，每当**positiveclass** 很少或者你**更关心false positives**而不是**false negatives**时，我们更倾向于使用PR曲线，否则使用ROC曲线。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们训练一个**RandomForestClassifier**并将它的ROC曲线和ROC AUC得分与**SGDClassifier**：\n",
    "\n",
    "* 首先，我们需要获得训练集中每个实例的分数。 \n",
    "* 但由于它的工作方式（见第7章），RandomForestClassi fier类没有decision_function（）方法。 \n",
    "* 相反，它有一个**predict_proba（）**方法。 Scikit-Learn分类器通常有一个或另一个。\n",
    "* **predict_proba（）**方法返回一个数组，这个数组的每一行包含一个实例，每一列代表一个类，每列包含给定实例属于给定类的概率（例如，图像内容表示5的概率为70％）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但要绘制ROC曲线，我们需要的是得分，而不是概率。 一个简单的解决方案是使用正类的概率作为分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure roc_curve_comparison_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213abee630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It is useful to plot the first ROC curve as well to see how they compare\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.plot(fpr, \n",
    "         tpr, \n",
    "         \"b:\", \n",
    "         linewidth=2, label=\"SGD\")\n",
    "\n",
    "plot_roc_curve(fpr_forest, \n",
    "               tpr_forest, \n",
    "               \"Random Forest\")\n",
    "\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "save_fig(\"roc_curve_comparison_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9931243366003829"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 从图中可以看到  **RandomForestClassifier** 的 ROC 曲线要比**SGDClassifier** 的看起来好多了: **it comes much closer to the top-left corner**.所以它的 ROC AUC score 明显的好很多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算**precision and recall scores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9852973447443494"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n",
    "precision_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8282604685482383"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "到现在我们已经知道如何训练二元分类器：\n",
    "* 为您的任务选择适当的指标，\n",
    "* 使用交叉验证评估您的分类器，\n",
    "* 选择适合您需求的精确/召回权衡，\n",
    "* 使用ROC曲线和ROC AUC分数比较各种模型。\n",
    "\n",
    "**以上我们尝试检测的仅仅是5s，这属于二分类，结果只有两个：是或者不是。现在我们来解决多分类的问题**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.  Multiclass Classification-多类分类\n",
    "\n",
    "一些算法（例如随机森林分类器或朴素贝叶斯分类器）是能够直接处理多个类。其他算法（例如支持向量机分类器或线性分类器）则是严格的二元分类器。但是，您可以使用各种策略来使用多个二元分类器执行多类分类。\n",
    "\n",
    "例如：\n",
    "* 一种方法是创建一个通过训练10个二元分类器，进而可以将数字图像分类为10个类（从0到9）的系统，每个数字对应一个分类器（0检测器，1检测器，2检测器等等）。然后，当您想要对图像进行分类时，您将从该图像的每个分类器中获得决策分数，并选择其分类器输出分数最高的类。这被称为**one-versus-all (OvA)**策略（也称为**one-versus-the-rest**）。\n",
    "* 另一种方法是为**每对数字**训练一个二元分类器：一个用于区分0和1，另一个用于区分0和2，另一个用于区分1和2，依此类推。这称为**one-versus-one (OvO)** 策略。如果有N个类别，则需要训练**N×（N - 1）/ 2** 个分类器。对于MNIST问题，这意味着训练45个二元分类器！当您想要对图像进行分类时，您必须通过所有45个分类器运行图像，并查看哪个类赢得了最多的决斗。 OvO的**主要优点**是每个分类器只需要训练必须区分的两个类的训练集。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some algorithms (such as Support Vector Machine classifiers) scale poorly with the size of the training set,因此对于这些算法，OvO是优选的，**因为在小训练集上训练许多分类器比在大训练集上训练少量分类器更快**。然而，对于大多数二元分类算法，OvA是优选的。当您尝试将二元分类算法用于多类分类任务时，Scikit-Learn检测到，并自动运行OvA（除了使用OvO的SVM分类器）。 让我们用SGDClassifier尝试一下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面两行代码使用0到9（y_train）的原始目标类训练训练集上的SGDClassifier，而不是**5-versus-all**目标类（y_train_5）。然后它进行预测（在这种情况下是正确的）。在幕后，Scikit-Learn实际上训练了10个二元分类器，得到了他们对图像的决策分数，并选择了得分最高的类。\n",
    "\n",
    "要确定情况确实如此，您可以调用decision_function（）方法。它现在返回**10个分数**，每个类别一个，而不是每个实例仅返回一个分数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-311402.62954431, -363517.28355739, -446449.5306454 ,\n",
       "        -183226.61023518, -414337.15339485,  161855.74572176,\n",
       "        -452576.39616343, -471957.14962573, -518542.33997148,\n",
       "        -536774.63961222]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = sgd_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最高分确实是对应于第5类的分数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果您想强制让ScikitLearn使用一对一或一对一，您可以使用**OneVsOneClassifier**或**OneVsRestClassifier**类。只需创建一个实例并将二元分类器传递给它的构造函数。例如，下面的代码基于SGDClassifier使用OvO策略创建多类分类器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "\n",
    "ovo_clf = OneVsOneClassifier(SGDClassifier(max_iter=5, random_state=42))\n",
    "ovo_clf.fit(X_train, y_train)\n",
    "ovo_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovo_clf.estimators_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练一个 RandomForestClassifier也很简单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\baideqian\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([5.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.fit(X_train, y_train)\n",
    "forest_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这次Scikit-Learn没有运行OvA或OvO，因为**随机森林分类器可以直接将实例分类为多个类**。我们可以调用**predict_proba（）**来获取分类器为每个类分配给每个实例的概率列表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1, 0. , 0. , 0.1, 0. , 0.8, 0. , 0. , 0. , 0. ]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.predict_proba([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您可以看到分类器对其预测非常有信心：数组中第5个索引处的0.8意味着模型估计图像表示为5的概率为80％。它还认为图像可以是0或3（每个10％的概率）。\n",
    "\n",
    "现在我们可以使用交叉验证来评估模型了， 我们可以使用**cross_val_score()** 评估 SGDClassifier ’s 的准确率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84063187, 0.84899245, 0.86652998])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准确率超过80%，如果只是使用一个随机分类器，准确率只有10%，所以这不是一个很差的结果，但是我们可以做的更好，比如，对输入进行简单的缩放，可以将上面的准确率提高到90%："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91011798, 0.90874544, 0.906636  ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Error Analysis-误差分析\n",
    "\n",
    "如果这是一个真实的项目，你将按照你的机器学习项目清单中的步骤：\n",
    "\n",
    "* 探索数据准备选项，\n",
    "* 尝试多个模型，\n",
    "* 筛选最好的模型\n",
    "* 使用**GridSearchCV**微调他们的超参数，\n",
    "\n",
    "正如我们在上一章中所做的那样，尽可能地自动化。在这里，我们假设您已找到一个有前途的模型，并且您想找到改进它的方法。 一种方法是分析它所犯的错误类型：\n",
    "\n",
    "* 首先，您可以查看混淆矩阵。您需要使用**cross_val_predict（）**函数进行预测，\n",
    "* 然后调用**confusion_matrix（）**函数，就像你之前做的那样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5725,    3,   24,    9,   10,   49,   50,   10,   39,    4],\n",
       "       [   2, 6493,   43,   25,    7,   40,    5,   10,  109,    8],\n",
       "       [  51,   41, 5321,  104,   89,   26,   87,   60,  166,   13],\n",
       "       [  47,   46,  141, 5342,    1,  231,   40,   50,  141,   92],\n",
       "       [  19,   29,   41,   10, 5366,    9,   56,   37,   86,  189],\n",
       "       [  73,   45,   36,  193,   64, 4582,  111,   30,  193,   94],\n",
       "       [  29,   34,   44,    2,   42,   85, 5627,   10,   45,    0],\n",
       "       [  25,   24,   74,   32,   54,   12,    6, 5787,   15,  236],\n",
       "       [  52,  161,   73,  156,   10,  163,   61,   25, 5027,  123],\n",
       "       [  43,   35,   26,   92,  178,   28,    2,  223,   82, 5240]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用Matplotlib的**matshow（）**函数来查看表示混淆矩阵的图像通常更方便："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(matrix):\n",
    "    \"\"\"If you prefer color and a colorbar\"\"\"\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(matrix)\n",
    "    fig.colorbar(cax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure confusion_matrix_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213abee3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "save_fig(\"confusion_matrix_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个混淆矩阵看起来相当不错，因为大多数图像都在主对角线上，这意味着他们被正确分类。5s看起来比其他数字稍暗，这可能意味着数据集中5s的图像较少，或者分类器在5s上的表现不如其他数字。 实际上，您可以验证两者都是如此。\n",
    "\n",
    "让我们把重点放在错误上：\n",
    "* 首先，您需要将混淆矩阵中的每个值除以相应类中的图像数量，这样您就可以比较错误率而不是绝对错误数（这会使丰富的类看起来不那么坏）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx / row_sums"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用**0**填充对角线以仅保留错误，并绘制结果;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure confusion_matrix_errors_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213ace7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用0填充对角线以仅保留错误\n",
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "save_fig(\"confusion_matrix_errors_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以清楚地看到分类器产生的错误类型。需要记住的是，**行表示实际的类**，而**列表示预测的类**：\n",
    "* 类8和9所在的**列**非常明亮，它告诉您许多图像被错误分类为8或9。\n",
    "* 类似地，类8和9所在的**行**也非常明亮，告诉你8s和9s经常与其他数字混淆。\n",
    "\n",
    "相反，有些**行**非常暗：\n",
    "* 例如第1行：这意味着大多数1都被正确分类（少数与8s混淆，但这就是它）。\n",
    "\n",
    "请注意，错误并不完全对称：\n",
    "* 例如，有更多的5被错误分类为8s而不是更多的8 被分类为5。\n",
    "\n",
    "分析混淆矩阵通常可以为您改进分类方法提供见解。根据这个图，我们的注意力似乎应该花在改进上分类8s和9s上，以及修复特定的3/5混乱的情况：\n",
    "* 例如，您可以尝试为这些数字收集更多训练数据\n",
    "* 或者您可以设计有助于分类器的新特征 - 例如，编写算法来计算闭环的数量（例如，8有两个，6有一个，5没有）。\n",
    "* 或者您可以预处理图像（例如，使用Scikit-Image，Pillow或OpenCV）使某些图案更突出，例如闭环。\n",
    "\n",
    "**分析个别错误**也可以是一个很好的方式来获得你的分类器正在做什么以及它失败的原因的见解，但它**更困难和耗时**。例如，让我们以绘制3s和5s为例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure error_analysis_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22116746ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_a, cl_b = 3, 5\n",
    "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
    "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
    "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
    "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n",
    "save_fig(\"error_analysis_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "左侧的两个5×5块显示分类为3的数字，右侧两个5×5块显示图像分类为5s。分类器出错的一些数字（即，在左下方和右上方的区域中）写得非常糟糕，甚至是人类将它们分类都有困难（例如，第8行和第1列的5真正看起来像3）。\n",
    "\n",
    "然而，大多数错误分类的图像对我们来说似乎是明显的错误，并且很难理解分类器为什么会犯错误。**原因**是我们使用了一个简单的SGDClassifier，它是一个线性模型。它所做的只是为每个像素分配每个类别的权重，当它看到一个新图像时，它**只是将加权像素强度相加得到每个类别的分数**。因此，**由于3s和5s仅相差几个像素**，因此该模型很容易混淆它们。\n",
    "\n",
    "3s和5s之间的主要区别在于连接的小线的位置顶部到底部弧线。如果你绘制一个3，结点稍微偏向左边，分类器可能将其分类为5，反之亦然。换句话说，这个分类器是对图像位移和旋转非常敏感。所以减少3/5混乱的一种方法将是预处理图像，以确保它们很好地居中，而不是旋转。这可能有助于减少其他错误。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.  Multilabel classification-多标签分类\n",
    "\n",
    "到目前为止，每个实例始终只分配给一个类。 在某些情况下，您可能希望分类器为每个实例输出多个类。例如，考虑一个人脸识别分类器：如果它识别出同一张图片中的几个人，该怎么办？ 当然，每个被识别的人应附上一个标签。假设分类器已被训练以识别三个面孔：Alice，Bob和Charlie; 然后当它显示Alice和Charlie的图片时，它应该输出**[1,0,1**]（意思是“Alice yes，Bob no，Charlie yes”）。 这种输出多个二元标记的分类系统称为**多标记分类系统**。\n",
    "\n",
    "我们暂时不会进行面部识别，但让我们看一个更简单的例子，仅用于说明目的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train % 2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此代码创建一个**y_multilabel**数组，这个数组包含每个数字图像的两个目标标签：第一个指示数字是否大（7,8或9），第二个表示它是否是奇数。 下一行创建一个**KNeighborsClassifier**实例（支持多标签分类，但不是所有分类器都支持），我们使用多个目标数组训练它。 现在您可以进行预测，并注意它输出的**两个标签**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数字5确实不大（False）同时也是奇数（True）\n",
    "\n",
    "评估多标记分类器的方法有很多种，选择正确的指标实际上取决于您的项目。 例如，一种方法是测量每个单独标签（或前面讨论的任何其他二元分类器度量）的**$F_1$分数**，然后简单地计算平均分数。 此代码计算所有标签的平均$F_1$分数：\n",
    "\n",
    "这个代码可能会运行很长时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97709078477525"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3, n_jobs=-1)\n",
    "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这假设所有标签都同样重要，但情况可能并非如此。特别是，如果你有比Charlie或Bob更多的Alice照片，你可能想在Alice的照片上更加重视分类器的分数。\n",
    "一个简单的选择是给每个标签一个等于其支持的权重（即具有该目标标签的实例数）。 要做到这一点，只需在前面的代码中设置average =“weighted”。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.  Multioutput Classification-多输出分类\n",
    "我们将在这里讨论的最后一种分类任务称为**多输出多类分类**（或简称多输出分类）。它只是**多标签分类**的一般化，其中每个标签可以是多类的（即，它可以具有两个以上的可能值）。\n",
    "\n",
    "为了说明这一点，让我们构建一个从图像中去除噪声的系统。 它将采取输入一个有噪声的数字图像，它（希望）输出一个干净的数字图像，表示为像素强度数组，就像MNIST图像一样。请注意，分类器的输出是**多标签**（每个像素一个标签），每个标签可以**有多个值**（像素强度范围从0到255）。 因此，它是多输出分类系统的一个例子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们首先通过获取MNIST图像来创建**训练**和**测试集**，使用NumPy的**randint（）** 函数将噪声添加到像素强度。目标图像将是原始图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise\n",
    "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们看看**测试集**中的图像（是的，我们正在窥探测试数据，所以你现在应该皱眉）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure noisy_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213acf6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_index = 5500\n",
    "plt.subplot(121); plot_digit(X_test_mod[some_index])\n",
    "plt.subplot(122); plot_digit(y_test_mod[some_index])\n",
    "save_fig(\"noisy_digit_example_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "左边是噪声输入图像，右边是干净的目标图像。 现在让我们训练分类器，让它清理这个图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure cleaned_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213ab2ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
    "plot_digit(clean_digit)\n",
    "save_fig(\"cleaned_digit_example_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来足够接近目标！到此我们结束了我们的分类之旅。希望你现在应该知道\n",
    "* 如何为分类任务选择好的指标\n",
    "* 选择适当的精度/召回权衡，\n",
    "* 选择适当的比较分类器，\n",
    "* 以及更一般地为各种任务建立良好的分类系统。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra material\n",
    "### Dummy (ie. random) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dmy_clf = DummyClassifier()\n",
    "y_probas_dmy = cross_val_predict(dmy_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
    "y_scores_dmy = y_probas_dmy[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213aa53940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fprr, tprr, thresholdsr = roc_curve(y_train_5, y_scores_dmy)\n",
    "plot_roc_curve(fprr, tprr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=-1, n_neighbors=4, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_clf = KNeighborsClassifier(n_jobs=-1, weights='distance', n_neighbors=4)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_knn_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b2d19379e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage.interpolation import shift\n",
    "def shift_digit(digit_array, dx, dy, new=0):\n",
    "    return shift(digit_array.reshape(28, 28), [dy, dx], cval=new).reshape(784)\n",
    "\n",
    "plot_digit(shift_digit(some_digit, 5, 1, new=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300000, 784), (300000,))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_expanded = [X_train]\n",
    "y_train_expanded = [y_train]\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    shifted_images = np.apply_along_axis(shift_digit, axis=1, arr=X_train, dx=dx, dy=dy)\n",
    "    X_train_expanded.append(shifted_images)\n",
    "    y_train_expanded.append(y_train)\n",
    "\n",
    "X_train_expanded = np.concatenate(X_train_expanded)\n",
    "y_train_expanded = np.concatenate(y_train_expanded)\n",
    "X_train_expanded.shape, y_train_expanded.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意**：下面的代码运行时间较长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=-1, n_neighbors=4, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_expanded, y_train_expanded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意**：下面的代码运行时间较长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_expanded_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_knn_expanded_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.       , 0.       , 0.5053645, 0.       , 0.       , 0.       ,\n",
       "        0.       , 0.4946355, 0.       , 0.       ]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ambiguous_digit = X_test[2589]\n",
    "knn_clf.predict_proba([ambiguous_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b297211048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(ambiguous_digit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    " ### 1. An MNIST Classifier With Over 97% Accuracy\n",
    "Try to build a classifier for the MNIST dataset that achieves **over 97%** accuracy on the test set. **Hint**: the **KNeighborsClassifier** works quite well for this task; **you just need to find good hyperparameter values** (try a **grid search** on the **weights** and  **n_neighbors** hyperparameters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  18 | elapsed: 173.4min remaining: 138.7min\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed: 229.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid=[{'n_neighbors': [3, 4, 5], 'weights': ['uniform', 'distance']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=3)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "# 网格搜索\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=3, verbose=3, n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4, 'weights': 'distance'}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9718166666666667"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = grid_search.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data Augmentation\n",
    "\n",
    "* Write a function that can shift an MNIST image in any direction (left, right, up, or down) by one pixel. \n",
    "* Then, for each image in the training set, create four shifted copies (one per direction) and add them to the training set. \n",
    "* Finally, train your best model on this expanded training set and measure its accuracy on the test set.\n",
    "\n",
    "You should observe that your model performs even better now! This technique of artificially growing the training set is called **data augmentation** or **training set expansion**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_image(image, dx, dy):\n",
    "    image = image.reshape((28, 28))\n",
    "    shifted_image = shift(image, [dy, dx], cval=0, mode=\"constant\")\n",
    "    return shifted_image.reshape([-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2211725b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = X_train[1000]\n",
    "shifted_image_down = shift_image(image, 0, 5)\n",
    "shifted_image_left = shift_image(image, -5, 0)\n",
    "\n",
    "plt.figure(figsize=(12,3))\n",
    "plt.subplot(131)\n",
    "plt.title(\"Original\", fontsize=14)\n",
    "plt.imshow(image.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(132)\n",
    "plt.title(\"Shifted down\", fontsize=14)\n",
    "plt.imshow(shifted_image_down.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(133)\n",
    "plt.title(\"Shifted left\", fontsize=14)\n",
    "plt.imshow(shifted_image_left.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_augmented = [image for image in X_train]\n",
    "y_train_augmented = [label for label in y_train]\n",
    "\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    for image, label in zip(X_train, y_train):\n",
    "        X_train_augmented.append(shift_image(image, dx, dy))\n",
    "        y_train_augmented.append(label)\n",
    "\n",
    "X_train_augmented = np.array(X_train_augmented)\n",
    "y_train_augmented = np.array(y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_idx = np.random.permutation(len(X_train_augmented))\n",
    "X_train_augmented = X_train_augmented[shuffle_idx]\n",
    "y_train_augmented = y_train_augmented[shuffle_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_augmented, y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By simply augmenting the data, we got a 0.5% accuracy boost. :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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